philosophy of SCIENCE – epistemology 2022 Quantum Wittgenstein – Metaphysical debates in quantum physics don’t get at ‘truth’ – they’re nothing but a form of ritual, activity and culture

…”…As a scientist and mathematician, Wittgenstein has challenged my own tendency to seek out interpretations of phenomena that have no scientific value – and to see such explanations as nothing more than narratives. He taught that all that philosophy can do is remind us of what is evidently true. It’s evidently true that the wavefunction has a multiverse interpretation, but one must assume the multiverse first, since it cannot be measured. So the interpretation is a tautology, not a discovery.

I have humbled myself to the fact that we can’t justify clinging to one interpretation of reality over another. In place of my early enthusiastic Platonism, I have come to think of the world not as one filled with sharply defined truths, but rather as a place containing myriad possibilities – each of which, like the possibilities within the wavefunction itself, can be simultaneously true. Likewise, mathematics and its surrounding language don’t represent reality so much as serve as a trusty tool for helping people to navigate the world. They are of human origin and for human purposes.

To shut up and calculate, then, recognises that there are limits to our pathways for understanding. Our only option as scientists is to look, predict and test. This might not be as glamorous an offering as the interpretations we can construct in our minds, but it is the royal road to real knowledge.

History of scienceQuantum theoryThinkers and theories 1-2022 What is a scientific theory? – A scientific theory is based on careful examination of facts. by By Alina Bradford , Ashley Hamer

>knowledge epistemology science idealism realism objectivity Realism in science needs to be more real – Realism in science cannot be completely unmoored from human experience. Otherwise, realism ends up tortured with unreal paradoxes. by Adam Frank

  • Science has a lot to say about the nature of reality. 
  • However, the problem with realism in science is that it has come to favor abstractions over everyday experience. 
  • Science is objective because it allows us to create maps that we can test together by comparing them with the results of experiments. This is real realism.

…”From this point of view, science is not objective because it points to some ideal God’s-Eye-View fairyland. Instead, science is objective because it allows us to create maps that we can test together by comparing them with the results of experiments…”…

reusable block > big data, AI, non theory science PHIL O SCIENCE 9/1/2022 Are we witnessing the dawn of post-theory science? Does the advent of machine learning mean the classic methodology of hypothesise, predict and test has had its day? by Laura Spinney

In 2008, Chris Anderson, the then editor-in-chief of Wired magazine, predicted its demise. So much data had accumulated, he argued, and computers were already so much better than us at finding relationships within it, that our theories were being exposed for what they were – oversimplifications of reality. Soon, the old scientific method – hypothesise, predict, test – would be relegated to the dustbin of history. We’d stop looking for the causes of things and be satisfied with correlations. With the benefit of hindsight, we can say that what Anderson saw is true (he wasn’t alone). The complexity that this wealth of data has revealed to us cannot be captured by theory as traditionally understood. “We have leapfrogged over our ability to even write the theories that are going to be useful for description,” says computational neuroscientist Peter Dayan, director of the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. “We don’t even know what they would look like.”

But Anderson’s prediction of the end of theory looks to have been premature – or maybe his thesis was itself an oversimplification. There are several reasons why theory refuses to die, despite the successes of such theory-free prediction engines as Facebook and AlphaFold. All are illuminating, because they force us to ask: what’s the best way to acquire knowledge and where does science go from here?

The first reason is that we’ve realised that artificial intelligences (AIs), particularly a form of machine learning called neural networks, which learn from data without having to be fed explicit instructions, are themselves fallible. Think of the prejudice that has been documented in Google’s search engines and Amazon’s hiring tools.The second is that humans turn out to be deeply uncomfortable with theory-free science. We don’t like dealing with a black box – we want to know why.

And third, there may still be plenty of theory of the traditional kind – that is, graspable by humans – that usefully explains much but has yet to be uncovered.

So theory isn’t dead, yet, but it is changing – perhaps beyond recognition. “The theories that make sense when you have huge amounts of data look quite different from those that make sense when you have small amounts,” says Tom Griffiths, a psychologist at Princeton University.

Griffiths has been using neural nets to help him improve on existing theories in his domain, which is human decision-making. A popular theory of how people make decisions when economic risk is involved is prospect theory, which was formulated by behavioural economists Daniel Kahneman and Amos Tversky in the 1970s (it later won Kahneman a Nobel prize). The idea at its core is that people are sometimes, but not always, rational.

Science last June, Griffiths’s group described how they trained a neural net on a vast dataset of decisions people took in 10,000 risky choice scenarios, then compared how accurately it predicted further decisions with respect to prospect theory. They found that prospect theory did pretty well, but the neural net showed its worth in highlighting where the theory broke down, that is, where its predictions failed.

These counter-examples were highly informative, Griffiths says, because they revealed more of the complexity that exists in real life. For example, humans are constantly weighing up probabilities based on incoming information, as prospect theory describes. But when there are too many competing probabilities for the brain to compute, they might switch to a different strategy – being guided by a rule of thumb, say – and a stockbroker’s rule of thumb might not be the same as that of a teenage bitcoin trader, since it is drawn from different experiences.

“We’re basically using the machine learning system to identify those cases where we’re seeing something that’s inconsistent with our theory,” Griffiths says. The bigger the dataset, the more inconsistencies the AI learns. The end result is not a theory in the traditional sense of a precise claim about how people make decisions, but a set of claims that is subject to certain constraints. A way to picture it might be as a branching tree of “if… then”-type rules, which is difficult to describe mathematically, let alone in words.

The final objection to post-theory science is that there is likely to be useful old-style theory – that is, generalisations extracted from discrete examples – that remains to be discovered and only humans can do that because it requires intuition. In other words, it requires a kind of instinctive homing in on those properties of the examples that are relevant to the general rule. One reason we consider Newton brilliant is that in order to come up with his second law he had to ignore some data. He had to imagine, for example, that things were falling in a vacuum, free of the interfering effects of air resistance.

In Nature last month, mathematician Christian Stump, of Ruhr University Bochum in Germany, called this intuitive step “the core of the creative process”. But the reason he was writing about it was to say that for the first time, an AI had pulled it off. DeepMind had built a machine-learning program that had prompted mathematicians towards new insights – new generalisations – in the mathematics of knots.

In 2022, therefore, there is almost no stage of the scientific process where AI hasn’t left its footprint. And the more we draw it into our quest for knowledge, the more it changes that quest. We’ll have to learn to live with that, but we can reassure ourselves about one thing: we’re still asking the questions. As Pablo Picasso put it in the 1960s, “computers are useless. They can only give you answers.”   Wolfgang Pauli’s *Philosophical* Position on Quantum Mechanics and Angels
Paul Austin Murphy

…”The Swiss-American theoretical physicist Wolfgang Pauli (1900–1958) once stated (in a 1954 letter to Max Born) the following often-quoted words:

[O]ne should no more rack one’s brain about the problem of whether something one cannot know anything about exists all the same, than about the ancient question of how many angels are able to sit on the point of a needle. But it seems to me that Einstein’s questions are ultimately always of this kind.”

Despite the bluntness and irony of that passage, it can still be argued that Pauli had a philosophical position on the reality that some scientists, philosophers and laypeople believe (as it were) hides behind our observations, experiments, tests, etc. So Pauli’s position can itself be interpreted as a philosophical position. In other words, Pauli wasn’t just offering a philistine scream of “shut up and calculate!”. (This is somewhat parallel to, for example, eliminative materialists and ontic structural realists whom are often deemed to offer “anti-philosophical” and “scientistic” positions while at the very same time being philosophers themselves.)

More specifically, Pauli rejected the opposition between reality itself (or “ultimate reality”) and what we can can know about reality (as did Niels Bohr). In other words, knowing “how Nature is” amounts to no more than a metaphysician’s dream. All we actually have is “what we can say about Nature”. And, at the quantum-mechanical level, what we can say is what we can say with the mathematics — in conjunction with experiments, tests, predictions, observations, etc. Consequently, just about everything else is analogical and/or imagistic in nature. Indeed the analogical/imagistic stuff can — and often does — mislead us.

Of course it can be asked whether or not Pauli was really talking about something that “one cannot know anything about” — or just being very impatient. (It must be noted that Pauli wrote these words in 1954 — long after the “quantum revolution” of the 1920s and 1930s.) Similarly, how did Pauli himself know that we could never know these things?..”…   2001  Human Nature and the Limits of Science  – by John A. Dupre

“John Dupré warns that our understanding of human nature is being distorted by two faulty and harmful forms of pseudo-scientific thinking. Not just in the academic world but increasingly in everyday life, we find one set of experts seeking to explain the ends at which humans aim in terms of evolutionary theory, and another set of experts using economic models to give rules of how we act to achieve those ends. Dupré charges this unholy alliance of evolutionary psychologists and rational-choice theorists with scientific imperialism: they use methods and ideas developed for one domain of inquiry in others where they are inappropriate. He demonstrates that these theorists’ explanations do not work, and furthermore that if taken seriously their theories tend to have dangerous social and political consequences. For these reasons, it is important to resist scientism – an exaggerated conception of what science can be expected to do for us. To say this is in no way to be against science – just against bad science.   Dupré restores sanity to the study of human nature by pointing the way to a proper understanding of humans in the societies that are our natural and necessary environments. He shows how our distinctively human capacities are shaped by the social contexts in which we are embedded. And he concludes with a bold challenge to one of the intellectual touchstones of modern science: the idea of the universe as causally complete and deterministic. In an impressive rehabilitation of the idea of free human agency, he argues that far from being helpless cogs in a mechanistic universe, humans are rare concentrations of causal power in a largely indeterministic world.  Human Nature and the Limits of Science is a provocative, witty, and persuasive corrective to scientism. In its place, Dupré commends a pluralistic approach to science, as the appropriate way to investigate a universe that is not unified in form. Anyone interested in science and human nature will enjoy this book, unless they are its targets.”  2/7/2021  Essentialism and Traditionalism in Academic Research – CasP – R Kyger, Blair Fix

Civilization and the culture of science: Science and the shaping of modernity, 1795–1935, by Stephen Gaukroger.  Reviewed by Gabriel Finkelstein

Anti-scientism, technoscience and philosophy of technology: Wittgenstein and Lyotard       by Michael A. Peters    2019

“The truly apocalyptic view of the world is that things do not repeat themselves. It isn’t absurd, e.g., to believe that the age of science and technology is the beginning of the end for humanity; that the idea of great progress is a delusion, along with the idea that the truth will ultimately be known; that there is nothing good or desirable about scientific knowledge and that mankind, in seeking it, is falling into a trap. It is by no means obvious that this is not how things are.”

–Ludwig Wittgenstein (1969), Culture and Value, p. 56e

Jean-Francois Lyotard understood Wittgenstein’s anti-scientism in the context of the Austrian counter-enlightenment tradition which was deeply suspicious of the grand claim that the scientific method is superior to all other means of learning or gaining knowledge. Wittgenstein’s negative cultural outlook was conditioned by Spengler’s (1926) The Decline of the West and a deep pessimism about what science could achieve and what it could not. It could not, for instance, give us moral direction or deal with ethics. Beale and Kidd (2017) suggest that Wittgenstein’s anti-scientism ‘sheds light upon and reveals connections between some of the central areas of his thinking’ (p. 5). Wittgenstein held a negative attitude about the role of science in modern civilization and its overwhelming confidence that it can resolve all problems and that it is only a matter of time before it extends its frontiers to encompass the whole of life. Wittgenstein’s anti-scientism that characterizes his view of modern civilization is the cultural outlook that connects with the broader issues of naturalism and empiricism. As Anna Boncompagni (2018) points out in a review of Beale and Kidd, scientism for Wittgenstein also carries the corollaries:

…science has the right, if not the duty, to extend its dominion into any territory; the scientific method is ‘the’ method of inquiry par excellence; other disciplines, if they are to attain knowledge at all, ought to conform to the scientific method; any domain of human experience can and should be reduced to the natural, empirical domain of science,

Wittgenstein’s anti-scientism conditions those that embrace his work in philosophy of science. While completing a philosophy of science degree in the 1970s at the University of Canterbury I became interested in the Wittgenstein-inspired philosophers of science, in particular, Stephen Toulmin (1958, 1972), Paul Feyerabend (1975), Russell Norwood Hanson (1958) and Thomas Kuhn (1962). Toulmin was a student of Wittgenstein’s (and the physicist Dirac) and had embraced his skepticism of science and anti-rationalism. Toulmin’s Wittgenstein’s Vienna (Janik & Toulmin, 1973), coauthored with Allan Janik, was a strategic text for me that changed forever my view of Wittgenstein as a placeholder in Cambridge philosophy and the analytic tradition.

Feyerabend published several papers on Wittgenstein discussing The Philosophical Investigations (1953). Elizabeth Anscombe had provided Feyerabend with manuscripts of Wittgenstein’s later work which Feyerabend said ‘exercised a profound influence’ upon him. John Preston (2016) notes:

Feyerabend planned to study with Wittgenstein in Cambridge, and Wittgenstein was prepared to take him on as a student, but he died before Feyerabend arrived in England. Karl Popper became his supervisor instead…

Feyerabend became a strong critic of Popper’s critical rationalism and of any rationalist attempt to lay down rules for scientific method. Feyerabend’s (1975) Against Method proposed that science was based on ‘epistemological anarchism’ (‘anything goes’) which was historically more successful and creative than a rule-based system. As Feyerabend argues:

The idea that science can, and should, be run according to fixed and universal rules, is both unrealistic and pernicious. It is unrealistic, for it takes too simple a view of the talents of man and of the circumstances which encourage, or cause, their development. And it is pernicious, for the attempt to enforce the rules is bound to increase our professional qualifications at the expense of our humanity. In addition, the idea is detrimental to science, for it neglects the complex physical and historical conditions which influence scientific change … Naive falsificationism takes it for granted that the laws of nature are manifest and not hidden beneath disturbances of considerable magnitude. Empiricism takes it for granted that sense experience is a better mirror of the world than pure thought. Praise of argument takes it for granted that the artifices of Reason give better results than the unchecked play of our emotions. Such assumptions may be perfectly plausible and even true. Still, one should occasionally put them to a test. Putting them to a test means that we stop using the methodology associated with them, start doing science in a different way and see what happens. Case studies such as those reported in the preceding chapters show that such tests occur all the time, and that they speak against the universal validity of any rule. All methodologies have their limitations and the only ‘rule’ that survives is ‘anything goes’.

Kuhn while at the University of California at Berkeley was introduced to the works of Wittgenstein and Feyerabend by Stanley Cavell in the early 1960s and discussed his Structure with Feyerabend. Some scholars have remarked how the recent revival of pragmatism can be understood in the context of Wittgenstein’s anti-foundationalism (Hmiel, 2016). Certainly, this was a feature of Wittgenstein’s thought, along with his anti-representationalism based on a language as use conception. Wittgenstein’s anti-foundationalism also became the basis for social constructivism in sociology developed by the likes of Ernst von Glasersfeld and David Bloor.

When I read Jean-Francois Lyotard’s (1984) The Postmodern Condition entirely by accident the year it was published in English, I was really taken by Lyotard’s use of Wittgenstein’s ‘language games’ to analyze the social bond as a series of ‘phrase regimes’ as he put it later in The Differend (Lyotard, 1988). I saw his interpretation as a form of creative appropriation rather than a scholarly reading based on textual analysis. What interested me was Lyotard’s use of Wittgenstein and the French reception more generally. It seemed to me a way out of the straight jacket of conceptual analysis of the so-called ‘London School’ in philosophy of education led by R. S. Peters and Paul Hirst that was allegedly based on ‘the revolution in philosophy’ introduced by Wittgenstein and others. Yet, I found the London School interpretation totally alien and could not understand how Peters and his colleagues could practice philosophy as a form of foundationalist conceptual analysis or ‘hygiene’ based on the search for necessary and sufficient conditions for the use of educational concepts. At the same time I recognize that this conception and interpretation of Wittgenstein by R. S. Peters and others actually provided and encouraged a robust analysis of concepts and a philosophy of education that while attributed to Wittgenstein against the spirit of his philosophy did achieve some significant gains for the field and provided some now classic texts like Ethics and Education that helped to reestablish the field in the late twentieth century.

In 1989, I wrote a paper entitled ‘Techno-science, Rationality and the University’ (Peters, 1989) as a discussion of Lyotard’s (1984) The Postmodern Condition based on an implicit understanding of Wittgenstein as an antifoundationalist thinker. I used the term ‘technoscience’ based on Lyotard’s use without too much thought at the time. It seemed to follow on quite naturally from Heidegger’s inversion of the traditional science/technology dualism and the model of applied science in his attempts to understand Western metaphysics as a form of techne as part of poesis and its use in connection with the concept of epistemology. (Techne is thus considered a kind of knowing from whence we derive ‘know-how’.) Lyotard’s use of the term also seemed to echo the tradition of French historical epistemology championed by Bachelard (1953) who popularized the term that characterized the long-term history of tool use. On this view technology only became combined with a nascent science during the seventeenth and eighteenth centuries during the European Enlightenment. Both Lyotard (1984) and Bruno Latour (1987) picked up on the term and used it in both a descriptive-analytical sense—the decisive role of technology-led science—and a critical-deconstructive sense to analyze scientific practices.   2021 “Life does not live,” reads the epigram that opens Minima Moralia by Theodor W. Adorno. In the age of its disintegration, in the context of fragmented reality, in which all master narratives have been shaken by an imponderable violence, planetary consciousness encounters existence in its incomprehensible singularity. As fragmented as the world she hopes to experience, cluttered with material and historical debris, philosophy is now faced with totalitarian unanimity, and she now chooses disintegration. To be a fragment among the fragments. A fragment that does not find in the other what interrupts it, but what continues it.

Imagined as a long letter, or as an endless conversation with the Friend, as well as with the Foreigner, philosophy experiences from its very inception the paradoxical condition of being at the same time in the search for a common eccentricity, a remote and unoccupied position, and, together with the other, for an inhabitable planet.